Typically, various technologies are known that enable streamlining of the task of converting voice data into a text (hereinafter, called a transcription task). For example, a technology is known in which, while a user is inputting characters during a transcription task, phrases which would be eventually input from the target voice data for transcription are estimated and presented to the user.
Moreover, a character input technology such as an input estimation technology or an estimation conversion technology is known in which, aside from displaying kana-kanji conversion candidates of a reading character string that has been input, the character string that is estimated to follow the reading character string is displayed as a conversion candidate character string (hereinafter, called estimation candidate).
However, every time the input candidate is to be presented, it is necessary to have the reading information ready. Hence, even in the case in which the user selects (accepts) the input candidate presented to him or her; it is still necessary to have the reading information ready for the purpose of presenting the next input candidate. Thus, after selecting the input candidate, the user again needs to perform character input, thereby leading to a decline in the work efficiency. Besides, regarding the voice data having low voice recognition accuracy, there is a possibility that incorrect input candidates are presented in succession. As a result, the input candidates become a hindrance to the user, thereby leasing to a decline in the work efficiency.
Moreover, in the conventional character input technology, input candidates are created only using a kana-kanji conversion dictionary in which reading character strings and post-kana-kanji-conversion characters are associated, and using character input history information. This leads to a decline in the work efficiency during a transcription task. Moreover, a candidate (hereinafter, called a follow-on candidate) that would follow a selected estimation candidate is searched in a conversion dictionary (an estimation conversion dictionary) dedicated to the character strings starting with user-selected estimation candidate. For this reason, in order to input long character strings (for example, in the units of sentences) in succession, it becomes necessary to hold long character strings in the estimation conversion dictionary, too. As a result, the size of the estimation conversion dictionary goes on increasing, thereby leading to a decline in the search efficiency of estimation candidates.